Keyword creation method and its apparatus

ABSTRACT

A keyword creation method and its apparatus to simply create keywords in user&#39;s retrieving a desired item of information from a vast amount of information. The habitual situation characteristics and the degree of typical liking tendency of a user are calculated on the basis of answers on daily items of the user, typical situation dependent keyword(s) of the user in one or more individual typical situations previously prepared is(are) created in accordance with the degree of typical liking tendency of the user and typical situation dependent keyword(s) is(are) revised in accordance with the habitual situation characteristics of the user, so that keyword(s) according to the actual situation of the user can be created.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to a keyword creation method andits apparatus, and is appropriately applied, for example, to a programretrieval system for retrieving the programs necessary for a viewer frommany TV programs distributed via a broadcasting satellite.

[0003] 2. Description of the Related Art

[0004] With a satellite broadcast system wherein TV programs aredistributed via a broadcasting satellite to viewer, TV signals aredigitalized and a vast number of programs are simultaneouslydistributed. In such a system, the number of programs selected by aviewer increases markedly.

[0005] Besides, with a system for providing various items of informationfrom the host side to computer terminals via a telephone line or privateline, a user on the terminal side selects the necessary item ofinformation from a vast amount of information and requests it to thehost side.

[0006] In an attempt to select such TV programs, items of informationusing a computer or the like, a viewer or user must retrieve a desiredprogram or information item from a vast number of programs or a vastamount of information. In this case, a viewer or user selects a word orthe like related to the genre of the program to be selected or theinformation item to be selected as the keyword and retrieves a desiredprogram or information item by referring to it.

[0007] In a way of a viewer or user to directly input a keyword to aretrieval system, however, a viewer or user need to always learn andrenew a knowledge concerning an up-to-date keyword or genreclassification method of information repeatedly according as programs orinformation items are renewed and has difficulty in readily selecting adesired keyword.

[0008] Besides, there is a method comprising storing keywords such asgenres or words selected by a viewer or user in the past as a selectionhistory and using them as keywords at the time of future retrieval. At afirst time of using a retrieval system according to this method, nohistory information is present and a viewer or user is compelled todirectly select and input a keyword to the system and has suchdifficulty in readily selecting a keyword as the above-mentioned case.

[0009] In such a manner, there was a problem that the retrievaloperation of a viewer or a user is complicated and it is difficult toreadily select a required program or information item.

SUMMARY OF THE INVENTION

[0010] In view of the foregoing, an object of the present invention isto provide a method and an apparatus for creating a keyword capable ofretrieving the information item corresponding to the liking of a vieweror user.

[0011] The foregoing object and other objects of the invention have beenachieved by inputting the answers of question items made to a user,calculating the habitual situation characteristics of the user and thedegree of a typical liking tendency of the user on the basis of theanswers, creating user's keyword(s) for typical situation(s) in one ormore previously prepared typical individual situation(s) on the basis-ofthe degree of user's typical liking tendency and correcting thekeyword(s) on the basis of user's habitual situation characteristics,thereby creating the keyword(s) corresponding to user's actualsituation.

[0012] According to the present invention, on the user's input of dailyitems such as his existing life stage and age/sex, his liking tendencyand living scene/selected site environmental phase, the keyword creationblock section creates the habitual situation conversion data related touser's habitual situation and the liking attribute ascribability datarelated to user's liking attribute, thereby automatically creating agroup of retrieval keywords reflecting the liking tendency of a userunder a specific situation in a specific field.

[0013] The nature, principle and utility of the invention will becomemore apparent from the following detailed description when read inconjunction with the accompanying in which like parts are designated bylike reference numerals or characters.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In the accompanying drawings:

[0015]FIG. 1 is a block diagram showing a satellite broadcast receivingsystem using a keyword creation unit according to the present invention;

[0016]FIG. 2 is a block diagram showing the configuration of anintegrated receiver/decoder (IRD) including the keyword creation unit;

[0017]FIG. 3 is a block diagram showing the keyword creation functionblock of the IRD;

[0018] FIGS. 4 to 7 are schematic diagrams showing an interaction screento a user;

[0019]FIGS. 8A and 8B are schematic diagrams showing examples ofhabitual situation conversion data;

[0020]FIG. 9 is a schematic diagram showing a simplified example ofliking attribute space;

[0021]FIG. 10 is a schematic diagram showing an example of likingattribute ascribability data array;

[0022]FIG. 11 is a schematic diagram showing an example of situationliking keyword of a user; and

[0023]FIG. 12 is a schematic diagram showing a specific situationkeyword group.

DETAILED DESCRIPTION OF THE EMBODIMENT

[0024] Preferred embodiment of the present invention will be describedwith reference to the accompanying drawings:

[0025] (1) General Configuration of a Satellite Broadcast ReceivingSystem

[0026] In FIG. 1, 1 denotes a satellite broadcast receiving system as awhole, while a broadcast signal received by a parabolic antenna 3 isdemodulated and decoded in compression by an integrated receiver/decoder(IRD) 2. The resultant image/voice signal SV1 is delivered to thesubsequent video cassette recorder (VCR) 6 of VHS type.

[0027] The VCR 6 records an image/voice signal SV1 onto a video tapeloaded inside or directly monitor-displays image/voice signal SV1 bydelivering it from an output line to a monitor device 4 as it is.

[0028] Besides, when a viewer manipulates a remote commander 5, theinstruction corresponding to the relevant manipulation is converted intoan infrared signal IR and delivered to the IRD 2. In accordance with therelevant instruction, the IRD 2 executes various operations, such aschannel switching, registration/readout of user data and delivery of acontrol signal CONT to individual appliances (VCR 6, VCR 7, DVD 8 and MD9) connected to the relevant IRD 2. A control signal CONT is deliveredvia a control line to the VCR 6. If the VCR 6 is specified by thiscontrol signal CONT as the control object, the VCR 6 is controlled bythe relevant control signal CONT. In contrast to this, if any of theappliances (VCR 7 of 8 mm type, digital video disc (DVD) player 8, minidisc (MD) player 9 and monitor device 4) successively connected to theVCR 6 via a control line is specified as the control object, the VCR 6delivers a control signal CONT to the subsequent VCR 7 of 8 mm type asit is.

[0029] On the input of a control signal CONT, the VCR 7 identifies theappliance specified by the control signal CONT. If the identified resultis the VCR 7, the VCR 7 executes the operation specified by the controlsignal CONT. If this direction is, for example, a direction for theplayback of an 8 mm video tape loaded on the VCR 7, the VCR 7 displaysit by the playback of the video tape and the delivery of a playbacksignal SV3 to the monitor device 4. Besides, if the direction by acontrol signal CONT is a direction for recording a broadcast signal(image/voice signal SV1) received and decoded by the IRD 2 in the VCR 7,the VCR 7 records the image/voice signal SV1 inputted from the IRD 2 viaa VCR 6 of VHS type and the monitor 4. In contrast to this, if thecontrol object of a control signal CONT is not the VCR 7, the VCR 7delivers the relevant control signal CONT to the subsequent DVD 8 as itis.

[0030] On the input of a control signal CONT, the DVD 8 identifies theappliance specified by the control signal CONT. If the identified resultis the DVD 8, the DVT 8 executes the operation specified by the controlsignal CONT. If this direction is, for example, a direction for theplayback of images or voices from the disk loaded on the DVD 8, the DVD8 displays it by the playback of the disk to deliver an image/voicesignal SV4 to the monitor device 4. In contrast to this, if the controlobject of a control signal CONT is not the DVD 8, the DVD 8 delivers therelevant control signal CONT to the subsequent MD 9 as it is.

[0031] On the input of a control signal CONT, the MD 9 identifies theappliance specified by the control signal CONT. If the identified resultis the MD 9, the MD 9 executes the operation specified by the controlsignal CONT. If this direction is, for example, a direction for theplayback of a disk loaded on the MD 9, the MD 9 gives off a voice signalfrom a speaker (not shown) mounted in the monitor device 4 by theplayback of the disk to deliver the voice signal SA1 to the monitordevice 4. Besides, if the direction by a control signal CONT is adirection for recording a voice signal SA2 in a broadcast signalreceived and decoded by the IRD 2 in the MD 9, the MD 9 records thevoice signal SA2 inputted from the IRD 2 via a VCR 6 of VHS type and themonitor device 4. In contrast to this, if the control object of acontrol signal CONT is not the MD 9, the MD 9 delivers the relevantcontrol signal CONT to the subsequent monitor device 4 as it is. At thattime, the monitor device 4 executes the operation specified by therelevant control signal CONT.

[0032] (2) Configuration of an IRD

[0033] In the IRD 2, as shown in FIG. 2, an RF signal outputted from thelow noise block downconverter (LNB) 3A of a parabolic antenna 3 is fedto a tuner 21 in the front end 20 and demodulated. An output of thetuner 21 is fed to a QPSK demodulator circuit 22 and QPSK-demodulated.An output of the QPSK demodulator circuit 22 is fed to an errorcorrection circuit 23, whose errors are detected and corrected, and isamended if necessary.

[0034] In a conditional access module (CAM) 33 comprising an IC cardmade of CPU, ROM and RAM, a cipher key is stored together with a decodedprogram. Since a signal transmitted via a broadcast satellite isenciphered, a key and cipher processing is required for deciphering thiscipher. Thus, this key is read out from the CAM 33 via a card readerinterface 32 and is fed to a demultiplexer 24. The demultiplexer 24deciphers an enciphered signal by using this key.

[0035] The demultiplexer 24 receives a signal outputted from the errorcorrection circuit 23 of the front end 20, feeds a deciphered videosignal to the MPEG video decoder 25 and feeds a deciphered audio signalto the MPEG audio decoder 26.

[0036] The MPEG video decoder 25 stores the inputted digital videosignal in the DRAM 25A and executes the decode processing of the videosignal compressed by the MPEG scheme. The decoded video signal is fed toan NTSC encoder 27 and converted into a brightness signal (Y), chromasignal (C) and composite signal (V) in the NTSC scheme. The brightnesssignal and chroma signal are outputted as S video signals via bufferamplifiers 28Y and 28C, respectively. Besides, the composite signal isoutputted via a buffer amplifier 28V.

[0037] The MPEG audio decoder 26 stores an audio digital signal fed fromthe demultiplexer 24 in a DRAM 26A and executes the decode processing ofan audio signal compressed by the MPEG scheme. The decoded audio signalis digital-to-analog converted in a D/A converter 30, the audio signalof the left channel is outputted via a buffer amplifier 31L and theaudio signal of the right channel is outputted via a buffer amplifier31R.

[0038] An RF modulator 41 converts the composite signal outputted by theNTSC encoder 27 and the audio signal outputted by the D/A converter 30into RF signals and outputs them. Besides, this RF modulator 41 allowsan RF signal of NTSC scheme inputted from other appliances to passthrough the modulator and outputs it to other appliances as it is.

[0039] In the case of this embodiment, these video and audio signals arefed to the VCR 6 via an AV line.

[0040] The CPU 29 executes various processing in accordance with theprogram stored in the ROM 37. Besides, the CPU 29 controls an AVappliance control signal transmitter/receiver section 2A, outputs apredetermined control signal to other appliances via a control line andreceives a control signal from other appliances.

[0041] Directly inputted to this CPU 29 can be a predeterminedinstruction by manipulating a manipulation button switch in the frontpanel 40. Besides, on the manipulation of a manipulation key in theremote commander 5, an infrared (IR) signal is outputted by the IRtransmitter section of the remote commander 5 and received by an IRreceiver section 39, and the received result is fed to the CPU 29.Accordingly, also by the manipulation of the remote commander 5, apredetermined instruction can be inputted to the CPU 29.

[0042] Besides, the CPU 29 takes in, for example, the electronic programguide (EPG) information except a video and an audio signal outputtedfrom the demultiplexer 24, makes out EPG data from it and feeds them toan static random access memory (SRAM) 36 and stores them. The EPGinformation includes information items (such as e.g., channel, time,title and genre of a program and program comment) about the programs ofindividual channels from the present time to tens of hours later. Sincethis EPG information item frequently comes by transmission, anup-to-date EPG information item is always retained in the SRAM 36.

[0043] The CPU 29 can transfer the data stored in the SRAM 36 to anexternal appliance via a modem 34 and communication means. Meanwhile, asa method for transferring data of the, SRAM 36 to an external appliance(floppy disk, card like recording medium, or the like), an output lineexclusively for data may be provided in addition to the communicationusing a modem.

[0044] And, in an electrically erasable programmable read only memory(EEPROM) 38, data desired to be retained even after the power off(rewritable data, such as e.g., receiving history for the past 4 weeksof a tuner 21 or data of the data base mentioned later (11A, 11B and11C)) are stored. Besides, comparing the time information outputted by acalendar timer 35 with the time stamp separated from a received signaland outputted by the demultiplexer 24, the CPU 29 controls the MPEGvideo decoder 25 or MPEG audio decoder 26 so as capable of conducting adecode processing at a proper timing.

[0045] Furthermore, when wanting to generate predetermined on-screendisplay (OSD) data, the CPU 29 controls the MPEG video decoder 25.Corresponding to this control, the MPEG video decoder 25 creates andwrites predetermined OSD data into a DRAM 25A and further reads out andoutputs them. Thereby, predetermined characters, pictures and suchothers can be outputted and displayed in the monitor device 4.

[0046] Here, when the manipulation key for program guide is selected inthe remote commander 5 or the front panel 40, the CPU 29 controls theMPEG video decoder 25 to display a broadcast program selection screen inthe monitor device 4. By moving the cursor to the position of a desiredprogram on this screen and clicking the remote commander 5, a user canselect and specify the desired program. At this time, with that programgenre corresponding to the liking of a user taken as a keyword which hasbeen created in advance in a keyword creation function block provided inthe IRD 2, the list of programs fit for the relevant user is displayedfrom numbers of programs.

[0047] Like this, FIG. 3 shows the creation function block for a keywordemployed in the retrieval of the program desired by a user in accordancewith the EPG information. That is, in FIG. 3, the user interfaceprocessing section 12 corresponds to the remote commander 5, the IRreceiver section 39 and the front panel 40 in the IRD 2 (FIG. 2), theanswer analysis processing section 13, the situation dependent likingkeyword creating section 14, specific situation liking keyword creatingsection 15 and the package title retrieval processing section 16correspond to the CPU 29 (FIG. 2) and the liking sect cluster dictionary11A, the liking-sect-dependent, situation dependent keyword group database 11B and the package title data base 11C correspond to the EEPROM38.

[0048] (3) Creation of a Keyword by the IRD

[0049]FIG. 3 shows the functional block of the portion related to thecreation of a keyword in the IRD 2 mentioned above referring to FIG. 2,and the user interface processing section 12 displays an interactionscreen for the creation of a keyword on the display screen 4A of themonitor device 4 (FIG. 1) by user's manipulation of the remote commander5. While specifying the answers for individual question items on thisinteraction screen by using a cursor, a user inputs a user profile forthe creation of a keyword.

[0050] These input items first of all include an item for the input ofgrowth stages of a user individual, such as “Advance to a university”,“Taking employment”, “Wedding”, “Bringing-up of a child” and“Retirement”, in which the relation of a user with his family andsociety is considered additionally, as the present life stage of theuser. In this case, an interaction screen as shown in FIG. 4 isdisplayed on the display screen 4A of the monitor device 4.

[0051] Secondly, the input items include an item for the input of anage/sex. In this case, an interaction screen as shown in FIG. 5 isdisplayed on the display screen 4A of the monitor 4.

[0052] Thirdly, the input items include an item concerning the likingtendency of a user. In this case, an interaction screen for specifying aplurality of liking tendencies as shown in FIG. 6 is displayed on thedisplay screen 4A of the monitor 4.

[0053] Fourthly, the input items include an item for the input of aliving scene such as “at breakfast”, “at lunch”, “at supper”, “at yourease on a weekday” and “at your ease on a holiday”, as livingscene/select site environmental phase of a user. In this case, a userinputs his own actual time range (referred to as environmental numericalvalue/region data) corresponding to each living scene on an interactionscreen as shown in FIG. 7 for each day of a week. As a result, data suchas “7:00-7:30 of Monday”, “7:30-8:00 of Saturday”, . . . are obtained,for example, as a living scene for “at breakfast”.

[0054] In such a manner, when a user's answer is inputted, the userinterface processing section 12 delivers the answer to the answeranalysis processing section 13. By pairing living scenes inputted by auser with individual time frame identifiers (situation identifiers)represented by the respective different identifiers and day-of-week timerange data (region data of environmental numerical values) peculiar tothe user corresponding to individual time frame identifiers, obtained onthe basis of the answer of a user, for each living scene, the answeranalysis processing section 13 obtains the habitual situation conversiondata of the user.

[0055]FIGS. 8A and 8B show examples of these habitual situationconversion data. That is, FIG. 8A comprises a data array with days of aweek and time made into correspondence to the time frame identifier(situation identifier) representing “at breakfast”. In this case, sincethe breakfast is taken in the same time range for a Monday to Friday,these data are represented by a product of data representing the rangeof days of a week (Monday-Friday) and data representing the range oftime (7:00-7:30) and further for Saturday where a breakfast is taken ata different time from that of these weekdays, they are represented by aproduct of data representing the range of the relevant day of a week(Saturday) and data representing the range of time (7:30-8:00). By a sumof individual data represented by such products of day-of-week rangedata and time range data, day-of-week/time range data (region data ofenvironmental numerical values) are obtained and habitual situationconversion data are obtained by a combination of these day-of-week/timerange data and time frame identifiers (situation identifiers).

[0056] Besides, FIG. 8B shows habitual situation conversion datacomprising a combination of the time frame identifier (situationidentifier) representing “at your ease on a holiday” andday-of-week/time range data and expresses that the living sceneidentifier of “at ease on a holiday” corresponds to the time range of8:00-11:30 both for Saturday and for Sunday. In such a manner, a timeframe identifier as the situation identifier established in conformityto the characteristics of a user is the name or number fordistinguishing a typical living scene affecting the selection of aprogram, affects the selection of a program independently of the likingtendency of a user and forms a factor to be selected in accordance withthe relevant moments and cases. Incidentally, in addition to a timeframe identifier, the situation identifiers include, for example, apartner situation identifier established in accordance with partnerscommon in situation to the relevant user and the common partners ofsituation include friends, lovers or the like. This partner situationidentifier is employed in a keyword creation for the selection of amusic program and music software.

[0057] Thus, habitual situation conversion data representing the customof a user, evaluated by a combination of time frame identifiers andregion data of environmental numerical values are stored once in theEEPROM 38 (FIG. 2).

[0058] Besides, the answer analysis processing section 13 evaluates aliking attribute ascribability data array as data representing theliking tendency of a user that changes depends on time and situation. Inthis case, an item on liking tendency inputted by a user to the userinterface processing section 12 is employed. This item is one inputtedfrom the interaction screen mentioned above in relation with FIG. 6. Byanswers to this, a plurality of liking attributes such as “knowledgedirectionality”, “activeness directionality”, “amusement directionality”and “relaxation directionality” influential on the selection of aprogram are obtained as the sense of attitude value of a user for TVviewing. Incidentally, at the time of keyword creation for the selectionof a music, items for obtaining directional tendency such as “specificgenre directioned”, “piece notion directioned”, “wide sound rangedirectioned” and “trend directioned” are given as questions to a user.

[0059] Thus, first based on the answers of a user concerning the likingtendency inputted to the user interface processing section 12, theanswer analysis processing section 13 evaluates the liking attributes ofthe user. That is, the answer analysis processing section 13 establishesthe respective directionalities concerning liking attributes such as“knowledge directionality”, “activeness directionality”, “amusementdirectionality” and “relaxation directionality”, obtained by the answersof a user, as values indicating individual directionalities on theattribute classification axes. Thereby, on the liking attributeclassification space formed by individual liking attributeclassification axes, the coordinates determined by individualdirectionalities serve as liking attribute vectors of a user and onepoint on the liking space determined by this attribute vector becomesthe liking attribute point indicating the liking tendency of this user.

[0060] Incidentally, FIG. 9 shows one example of liking attributeclassification space formed by three attribute classification axes, agelevel axis (Z-axis), activeness direction axis (X-axis) and knowledgedirection axis (Y-axis), while the liking attribute point P is evaluatedfrom the age, activeness directionality and knowledge directionalityobtained from the input of the user.

[0061] Here, when a plurality of liking attribute points are plotted inone liking attribute classification space with many users taken as thepopulation, there are cases where crowded collections (hereinafter,referred to as clusters) appear at several sites. The respectiveclusters correspond to collections of users having a similar likingattribute and a finite number of clusters are present in the likingattribute classification space which are not always exclusive. Theexamples of clusters include the knowledge attitude cluster CL1corresponding to a relaxed amusing sect, the knowledge attitude clusterCL2 corresponding to a knowledge desiring sect and the knowledgeattribute cluster CL3 corresponding to a trend pursuing sect asknowledge attitude clusters determined by the knowledge direction axis,the activeness direction axis and the age level axis shown in FIG. 9.Besides, there is also a case where clusters are formed in theprojection subspace using a part of the liking attribute classificationaxes. In this case, for example, age level clusters are formed in theprojection space using the age level axis.

[0062] Incidentally, in the liking attribute classification space forthe selection of a music, clusters corresponding to a mood fascinatingsect, a scream diverging sect and so on are formed.

[0063] The name or number employed for distinguishing these clusters arereferred to as a cluster identifier and the center of each cluster isreferred to as a cluster representative point. Here, the likingattribute point P corresponding to one user does not generally coincidewith the representative point of a cluster. Besides, one user isconsidered to have the liking attribute of the adjacent clusters to someextent. Thus, the degrees of the liking attribute of one user to beascribed to the respective adjacent clusters are expressed in anumerical array and this numerical array is defined as a likingattribute ascribability data array of the user.

[0064] Here, when data on the liking attribute point P of a user issettled, the degrees of ascribability to individual clusters aredetermined from the liking attribute point P and representative points,stretches and shapes of clusters. Among these, cluster representativepoints and stretches of clusters are not dependent on the likingattribute point P of the user at all and peculiar to the respectiveclusters. Thus, from a cluster representative point and a stretch aspectfor each cluster, the method for calculating the ascribability (likingattribute ascribability) to the respective clusters can be determined inadvance.

[0065] The method for calculating the ascribability (liking attributeascribability) to a cluster will be described below. To evaluate theascribability (liking attribute ascribability) to a certain cluster whenthe liking attribute point P of one user is settled, first, the errorvector between the liking attribute point P and the clusterrepresentative point is evaluated. Next, using a function thatmonotonously decreases with larger error vector (i.e., functiondepending on the stretch of a cluster), its value is calculated.

[0066] If the stretch aspect of the function employed for evaluatingthis liking attribute ascribability is independent of individual likingattribute classification axis direction and isotropic, the inverse of1.0 plus the square of the length (representing the distance of astretch) of an error vector normalized by a standard deviation of astretch (stretch deviation) or the like is set to a liking attributeascribability. In this case, a city block distance, maximum absolutevalue component or Euclid distance may be employed as the length of anerror vector.

[0067] Alternatively, if the stretch of a cluster differs withindividual liking attribute classification axes, the inverse of about1.0 plus the square of a norm having an (rectangular parallelopiped)axis-dependent weight with the inverse of a standard deviation for eachliking attribute classification axis taken as the weight coefficient forthe relevant axis (i.e. when the cluster regarded as a rectangularparallelepiped) is set to the liking attribute ascribability in place ofthe above-mentioned isotropic distance.

[0068] Alternatively, if a cluster stretches in a direction slant toliking attribute classification axes, the quotient of a definite numberby another definite number plus the ellipsoid norm (i.e. when thecluster regarded as an ellipsoid) of quadratic form using thecoefficients evaluated from covariance coefficients or the like is setto the liking attribute ascribability.

[0069] Incidentally, when the stretch of a cluster is complicated and ageneral function is necessary, a function wherein the convex polyhedronnorm using the maximum of finite number of linear expressions isutilized in place of the above-mentioned city block distance, a functionusing a neuro or lookup table or the like can be employed.

[0070] Various functions set as an ascribability calculation method inthis manner are previously stored in a cluster dictionary 11A (FIG. 3),data specified for this ascribability calculation method are therespective functions used for individual clusters in calculating theascribability of clusters and data for specifying what parameters toexecute these functions with, which are combinations of calculationfunction identifiers expressed in function pointer and calculationparameters such as cluster representative points and cluster stretchdegree. The calculation parameters are expressed in a data array,pointers to data structures or the like.

[0071] When the liking attribute point P of a user is settled by theanalysis of user's answers in the answer analysis processing section 13,calculation of a liking attribute ascribability data array used for thefunctions and parameters set in such a manner is executed in the answeranalysis processing section 13 while referring to the ascribabilitycalculation method specified data corresponding to individual clustersstored in the cluster dictionary 11A.

[0072] That is, to calculate a value of ascribability to one cluster,ascribability calculation method specified data for the cluster arefetched from the cluster dictionary 11A, the function specified by therelevant ascribability calculation method specified data is read outwith parameters serving as a part of calculation method specified dataand liking attribute point data resulting from the answer analysis beingemployed as augments and is executed. A functional value obtained as anexecution result of this function is a cluster ascribability value. Asuccessive substitution of ascribability values, obtained bysuccessively repeating this for all clusters, into array componentswould provide the liking attribute ascribability data array of the user.

[0073] Incidentally, the cluster dictionary 11A is not only provided inthe EEPROM 38 (FIG. 2), but can be also read in from a predeterminedrecording medium or downloaded from the communication line, stored inthe EEPROM 38 and used. In this case, the kind and calculation method ofclusters become updatable and further a new calculation scheme can beimplemented by updating a cluster dictionary together with theregistration and addition of a new function program.

[0074] Incidentally, FIG. 10 shows one example of liking attributeascribability data array, while in an array of ascribability to each agelevel, individual arrayed numerals represent the ascribability for therespective age levels (e.g., teens, twenties, thirties, . . .) andindividual arrayed numerals in an array of ascribability to each likingsect represent the ascribability for the respective liking sects (e.g.,knowledge desiring sect, trend pursuing sect, . . .). In this case, bylimiting individual arrayed numerals to “0” or “1”, a numeral signifiesa user either perfectly belonging to or completely being independent ofeach cluster.

[0075] In such a manner, when the liking attribute ascribability dataarray of a user is obtained in the answer analysis processing section13, the relevant attribute ascribability data array is delivered to thesituation dependent liking keyword creation section 14 (FIG. 3) togetherwith the above-mentioned habitual situation conversion data. Thesituation dependent liking keyword creation section 14 makes the likingattribute cluster corresponding to several highly ascribable members ofthe liking attribute ascribability data array into the stronglyascribable cluster of the user.

[0076] The situation dependent liking keyword creation section 14fetches the keyword corresponding to the relevant strongly ascribablecluster from the liking-sect-dependent, situation dependent keywordgroup database 11B. In this liking-sect-dependent, situation dependentkeyword group data base 11B, keywords included in liking titles (likingprogram genres) under various situations, of persons of varioustendencies are classified and stored.

[0077] That is, generally, typical users ascribed to each liking clusterlike titles (program genres) of a definite tendency under a typicalsituation. Thus, in the liking-sect-dependent, situation dependentkeyword group database 11B, a group of keywords frequently appearing inliking titles (program genres) or news items for introduction/summary isprepared previously for each situation classification and for eachliking class. Incidentally, at the creation of a keyword in theselection of a TV program, a name of program genre is prepared as afrequent keyword.

[0078] To each keyword prepared in the liking-sect-dependent, situationdependent keyword group database 11B, a liking degree is attached.

[0079] If at least one liking attribute cluster is specified, thisliking-sect-dependent, situation dependent keyword group data base 11Bis so arranged that a group of keyword/liking degree pairs divided foreach situation classification can be fetched. As an actual construction,a data base, a retrieval server (subroutine, thread and process) and soon are utilized.

[0080] Accordingly, depending on a typical situation represented by eachsituation classification identifier, the situation dependent likingkeyword creation section 14 successively fetches the situation likingkeyword group of the user corresponding to his strongly ascribablecluster from the liking-sect-dependent, situation dependent keywordgroup data base 11B. In general, there are a plurality of stronglyascribable clusters, so that a plurality of liking keyword groups areobtained also for a single situation. They are merged (lumped) into aset for each situation. As this way of merge, first, a collection ofkeywords is obtained by the collection and merge of keyword groups foreach cluster. Next, a liking degree paired to each keyword is calculatedfrom the liking degree attached to a cluster keyword and theascribability to the cluster if the keyword comes from the likingkeyword group of a unique cluster. The functional conditions for thiscalculation is a function having a weak monotonously increasing propertyfor both the original liking degree and ascribability.

[0081] For example, there are a method using a product of the likingdegree and ascribability, a method using an arithmetic mean, a methodusing a minimum, etc. Furthermore, a monotonously increasing function byutilizing a lookup table technique may be employed.

[0082] Next, on the assumption that the same keyword is included in theliking keyword group for a plurality of clusters, first, the likingdegree is evaluated solely for each cluster in accordance with one ofthe methods mentioned above and their sum or maximum is made into asynthesized liking degree.

[0083] In such a manner, by repeating these processing for eachsituation classification, the liking keyword group (program genre namegroup) for each situation concerning a specific user is obtained.

[0084] The keyword group obtained thus is stored and retained in theEEPROM 38 (FIG. 2). Besides, strongly ascribable cluster data for eachuser are also stored and retained in the EEPROM 38 and if a likingkeyword data base for each liking cluster situation(liking-sect-dependent, situation dependent data base of FIG. 3) isupdated, a liking keyword group for each situation for each user can beupdated by retrieving the updated data base again and synthesizing it inaccordance with the methods mentioned above.

[0085] Incidentally, FIG. 11 shows one example of situation dependentliking keywords created in the situation dependent liking keywordcreation section 14 and a program genre name group in each situation (atbreakfast, at rest, . . .) is created for each situation.

[0086] In such a manner, the situation dependent liking keyword group(FIG. 11) created in the situation dependent liking keyword creationsection 14 is delivered to the subsequent specific situation likingkeyword creation processing section 15. Here, the specific situationindicates the situation at a specific time point and is typicallyrepresented by a situation identifier, but becomes a complex ofsituations represented by a plurality of situation identifiers accordingto individual situations. Thus, employed as the representation ofspecific situations is an array of numerical values, representing thedegree of being close to individual typical situations (situationascribability) represented by situation identifiers. This situationascribability array will be referred to as situation ascribability dataarray.

[0087] This situation ascribability data array can be automaticallycreated by the relevant system or can be inputted to the system on thespot by a user via input means (user interface processing section 12).For example, the degree of time frame ascribability for discriminatingthe boundary neighborhood of a time frame on the basis of time isautomatically created by the CPU 29 (FIG. 2). In contrast to this, withrespect to partner situation or the like on the site, the ascribabilityto the relevant situation is settled as a result of user's input tospecify a situation by using an interaction screen.

[0088] On the basis of situation dependent liking keyword groupscorresponding to individual typical situations received from thesituation dependent liking keyword creation section 14, the specificsituation liking keyword creation section 15 evaluates the likingkeyword group of a specific user corresponding to a specific situationexpressed in a situation ascribability data array through the weightedsynthesis using an ascribability. In the weighted synthesis calculationfor obtaining the liking degree to be paired to each keyword, a productsum of situation ascribabilities and liking degrees for typicalsituations can be simply employed. The keyword collection with likingdegree obtained thus becomes a specific situation liking keyword groupof the specific user. Incidentally, as a technique of weighted synthesiscalculation for liking degree, a function that has a monotonuouslyincreasing property concerning all variables may be selected andemployed for synthesis.

[0089] In such a manner, as shown in FIG. 12, the specific situationkeyword group created in the specific situation liking keyword creationprocessing section 15 is delivered to the subsequent package titleretrieval processing section 16 and the corresponding title is retrievedfrom the package title data base 11C in accordance with the relevantspecific situation keyword group. With this embodiment, the EPG datatransmitted by a satellite broadcast is stored in the package title database 11C and the EPG data specified by a program genre created as aspecified situation keyword group is retrieved. On the display screen 4Aof the monitor device 4, a plurality of characters representing theprograms retrieved by these EPG data are displayed and a user can selectthe relevant program by specifying any of the relevant characters.

[0090] Incidentally, the content of the package title data base 11C isupdated for each fetch of a new EPC data, thus always retainingup-to-date data.

[0091] (4) Operation and Effect of the Embodiment

[0092] In the above arrangement, when a user inputs daily items such asuser's existing life stage, age/sex, user's liking tendency and user'sliving scene/selected site environmental phase by means of aninteraction screen displayed on the monitor screen, the keyword creationblock section (FIG. 3) of the IRD 2 creates habitual situationconversion data related to the habitual situation of the user and likingattribute ascribability data related to the liking attribute of the userand thereby creates a keyword group for retrieval reflecting the likingtendency of the user under a specific situation in a specific field.

[0093] Thus, only if, even without a professional knowledge on retrievalsuch as keywords always updated and up-to-date knowledge on genreclassification methods, a user answers a daily simple question on itemsrelated to habit and liking once, programs conforming to the situationpeculiar to the user and his liking are continuously retrieved from thattime.

[0094] Besides, only by rewriting the liking-sect-dependent, situationdependent keyword data base stored in memory means such as an EPPROM 38,up-to-date keywords can be treated immediately. Thereby, withoutlearning up-to-date keyword by heart, a user can always cope with theupdate of keywords.

[0095] Thus, according to the above arrangement, the load of a userconcerning retrieval can be greatly reduced.

[0096] (5) Other Embodiments

[0097] Incidentally, in the above embodiment, the case of inputting thelife stage, age/sex, liking tendency and living scene as input items ofa user has described, but the present invention is not only limited tothis case and the input items may be reduced to several of them or otheritems may be added.

[0098] Besides, in the above embodiment, the case where a keywordcreation block for information retrieval is provided inside the IRD 2for receiving a satellite broadcast has described, but the presentinvention is not only limited to this and a keyword creation unit may beprovided separately.

[0099] Furthermore, in the above embodiment, the case where the presentinvention was applied to a device for retrieving a program of digitalsatellite broadcast has described, but the present invention is not onlylimited to this and is widely applicable to the keyword creation unit ofvarious information retrieval apparatus such as, e.g., for retrieving avast amount of information by means of internet and retrieving items ofpackage information in a compact disk or the like.

[0100] As mentioned above, according to the present invention, thehabitual situation characteristics and the degree of typical likingtendency of a user are calculated on the basis of answers on daily itemsof the user, typical situation dependent keyword(s) of the user in oneor more individual typical situations previously prepared is(are)created in accordance with the degree of typical liking tendency of theuser and typical situation dependent keyword(s) is(are) revised inaccordance with the habitual situation characteristics of the user, sothat keyword(s) according to the actual situation of the user can becreated.

[0101] While there has been described in connection with the preferredembodiments of the invention, it will be obvious to those skilled in theart that various changes and modifications may be aimed, therefore, tocover in the appended claims all such changes and modifications as fallwithin the true spirit and scope of the invention.

What is claimed is:
 1. A keyword creation method comprising the stepsof: inputting the answers of question items given to a user; calculatingthe habitual situation characteristics of said user and the degree oftypical liking tendency of said user on the basis of said answers;creating a keyword of said user for each typical situation in one ormore previously prepared typical individual situations on the basis ofthe degree of typical liking tendency of said user; and correcting saidtypical situation dependent keyword on the basis of the habitualsituation characteristics of said user to thereby create the keywordcorresponding to the actual situation of said user.
 2. A keywordcreation apparatus comprising: input means for inputting the answers ofquestion items given to a user; calculation means for calculating thehabitual situation characteristics of said user and the degree oftypical liking tendency of said user on the basis of said answers;typical situation dependent keyword creation means for creating atypical situation dependent keyword of said user in one or morepreviously prepared typical individual situations on the basis of thedegree of typical liking tendency of said user; and specific situationkeyword creation means for correcting said typical situation dependentkeyword on the basis of the habitual situation characteristics of saiduser to thereby create the keyword corresponding to the actual situationof said user.
 3. The keyword creation apparatus according to claim 2,further comprising: a data base for storing collection data on samplesof general liking attribute points serving as an element for calculatingthe degree of a typical liking tendency of said user for saidcalculation means and ascription information calculation data for saidcollection information of said user.
 4. The keyword creation apparatusaccording to claim 3, wherein said data base has the stored data capableof being updated.
 5. The keyword creation apparatus according to claim2, further comprising: a keyword group data base for feeding atypical-situation dependent keyword to said typical situation dependentkeyword creation means on the basis of the degree of a typical likingtendency of said user.
 6. The keyword creation apparatus according toclaim 5, wherein: said keyword group data base has the stored datacapable of being updated.
 7. The keyword creation apparatus according toclaim 2, further comprising: retrieval means for retrieving apredetermined title in accordance with said specific situation keyword.8. The keyword creation apparatus according to claim 7, furthercomprising: a title data base for feeding the title corresponding tosaid specific situation keyword to said retrieval means.
 9. The keywordcreation apparatus according to claim 8, wherein: said title data basehas the stored data capable of being updated.
 10. The keyword creationmethod according to claim 1, further comprising: a data base step forstoring collection data on samples of general liking attribute pointsserving as an element for calculating the degree of a typical likingtendency of said user for said calculation step and ascriptioninformation calculation data for said collection information of saiduser.
 11. The keyword creation method according to claim 1, wherein:said data base step has the stored data capable of being updated. 12.The keyword creation method according to claim 1, further comprising: akeyword group data base step for feeding a typical-situation dependentkeyword to said typical situation dependent keyword creation step on thebasis of the degree of a typical liking tendency of said user.
 13. Thekeyword creation method according to claim 1, wherein: said keywordgroup data base step has the stored data capable of being updated. 14.The keyword creation method according to claim 1, further comprising thestep of: retrieving a predetermined title in accordance with saidspecific situation keyword.
 15. The keyword creation method according toclaim 1, further comprising: a title data base step for feeding thetitle corresponding to said specific situation keyword to said retrievalstep.
 16. The keyword creation method according to claim 1, wherein:said title data base step has the stored data capable of being updated.